Mobile devices such as smartphones and tablet PCs are more and more superseding traditional desktop PCs as the primary access medium to the Internet. The rising popularity of third-party applications, the mobile applications or apps, contribute to heavily increasing data volume in wireless communications networks. Apps for voice over IP, instant messaging, video on demand, social networking, push services such as weather services, newsletter services, etc. are gradually replacing and/or adding to well-established services such as SMS and MMS. According to typical configurations, between 30 and 100 apps may be installed on a smartphone.
The rapid growth of third-party applications increases the risk of said apps considerably affecting the use of a smartphone or tablet PC. Apps can, for instance, drain the battery, even when they are not actively used and users may then experience rapidly discharging batteries. Moreover, apps can create huge amounts of data and signaling traffic in an operator's LTE or WCDMA network. Therefore, the success of mobile apps exposes new issues that were not initially expected, namely on mobile network providers (MNOs) in terms of dynamic load management and on mobile device manufacturers in terms of battery life.
More specifically, applications intended to be installed on mobile devices are also called over the top (OTT) applications as they operate “over the top” of the network, which includes that they can be the cause of considerable data traffic not only or primarily with a view on bandwidth usage, but at least also with a view on the resource usage of the lower layers, where they can cause considerable radio signaling load, e.g., due to their periodic Heartbeat signals and push messages exchanged with the respective application servers and even when the phone and/or app is not being actively used. The signaling load may deteriorate services for normal voice calls, SMS etc. and has even led to network service disruptions (‘signaling storms’). A considerable amount of signaling can also have a significant impact on the battery life of app-carrying smartphones.
As opposed to wired and WiFi (WLAN) networks, cellular networks have a clearly separated control-plane for signaling traffic and user-plane for user traffic, respectively. However, the control plane may impact on user plane performance and vice-versa. Specifically, signaling messages on the control plane, e.g., Layer-2 and Layer-3 (L2/L3) messages may over/load and stress network infrastructure. For example, in order that an OTT application may exchange data in the user plane with the application service, which as such may comprise a small amount of IP data only, a radio communication link has to be established. This may include radio bearer (re)configuration, setup of access layer security, sending of service request messages, etc. Once connected, the smartphone remains in a mode in which it can exchange data with the network for a duration based on one or more network specific timers. Even if no further signaling is required, said mode consumes a considerable amount of energy at the mobile device.
It turns out challenging for the network operators and mobile device manufacturers to analyze network (over)load problems suspected to be due to OTT apps on mobile devices and to improve network response to smartphones with an ever increasing number of apps.
Network-centric system test approaches may comprise developing test scenarios primarily for the control plane, wherein a considerable signaling load supposed to be similar to that of a plurality of app-carrying smartphones is generated/simulated and is applied to a test network supposed to be configured similar to an existing network.
On a more fine-grained function-test level, use of known test equipment can be contemplated which can monitor a wireless interface between network and mobile device in the control plane, i.e., can interpret a signaling on said interface. The equipment is adapted for analyzing specific communications systems such as, e.g., the RRC (Radio Resource Control) links between RNC (Radio Network Controller) and base station (eNodeB) of LTE networks and mobile devices. However, normally higher layer user plane traffic, such as IP traffic, cannot be analyzed, therefore it is unclear how such test equipment can help with the analysis of impact of OTT applications on a network load.
Specific analyzer devices or tools are also available for monitoring, measuring and analyzing IP traffic. IP data packets can be assigned to individual applications, such that end-to-end data traffic and protocol analysis can be generated. However, again, it is unclear how such tools may assist in the analysis of network load due to OTT applications.
Narseo et al. “RILAnalyzer: a Compehensive 3G Monitor On Your Phone” (http://rilanalyzer.smart-e.org/) describes, as a handset-oriented approach, a software tool that provides mechanisms to perform network analysis from within a mobile device.
Low-level (control-plane) radio information and cellular network control-plane data can be recorded, as well as user-plane data, together with traffic load and network configurations.
“Over the top” (OTT) applications using location based services (LBS) contribute to considerable additional network traffic due to LBS location invocations and associated signaling and data traffic. LBS applications are used in the background by many types of OTT applications including navigation, tracking service, social networking, public safety, and information services. LBS applications use additional hardware and satellite navigation system radios such as GPS, which have significant impact on the battery life of the mobile. Monitoring the behavior of LBS applications usually requires a moving mobile. Achieving such a movement in an indoor test environment is so far not possible.
Especially, so far a measurement of OTT LBS applications is therefore not possible under realistic conditions. Especially, it is so far not possible to measure control plane traffic such as RRC connection requests triggered by LBS location invocations and control plane mode RRC, NAS messages with LBS positioning protocol data. Also, so far it is no possible to measure user plane traffic, such as user plane mode LBS IP traffic carrying LBS protocol data (LPP, SUPL) and IP traffic due to map downloading and other related LBS data. Also, at present, it is not possible to determine additional power consumption by a satellite navigation receiver, mobile data (3G/4G) radio, CPU/processing power of maps, etc. It is especially problematic that power consumption is highly dependent on the operating system OS policies, a number of location invocations and a location accuracy.
OTT applications stress network infrastructure and affect service quality due to the frequent signaling and data connections they initiate, due to the amount of network resources they use and also due to the speed with which they drain the smartphone battery.
There is a need, therefore, for an approach for the analysis of the impact of over the top (OTT) applications, which operate “over the top” of the network applications, with respect to network performance and power consumption on mobile devices. Also, there is further a need for an approach that facilitates the determination of the impact of location based services on the performance of the mobile devices.